Evaluation of the BALANCE Program as a Digital Therapeutic Solution for Type 2 Diabetes Management: Protocol for a Prospective Lifestyle Intervention Study
Notice bibliographique
Résumé
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a growing global health concern. In 2016, 9.7% of Bruneian adults aged 18 to 69 years had diabetes, making it the third leading cause of death. Effective self-management can mitigate complications that require health care interventions and lower health care costs. Brunei Darussalam has deployed BruHealth, a national mobile health platform synced with the Brunei Health Information Management System, allowing patients to access health records, schedule appointments, and explore medical articles. A digital therapeutics module for T2DM (diabetes mellitus digital therapeutics [DM DTx]) has been developed, consisting of a digital lifestyle intervention module within BruHealth and a separate health care professional portal for health coaches. The 16-week BALANCE program aims to support self-management. This study explores the efficacy of DM DTx in managing T2DM through digital lifestyle interventions. OBJECTIVE: The primary objective is to determine the proportion of participants who achieve at least a 0.6% reduction in glycated hemoglobin after 16 weeks. Secondary objectives include evaluating changes in glycated hemoglobin, fasting lipid profile, blood glucose, BMI, and waist circumference and analyzing participant feedback. Given the predominantly Muslim population, the study also aims to gain insight into fasting practices for Muslim participants with T2DM. METHODS: This single-arm, nonrandomized intervention study involves adults aged 18 to 70 years with T2DM. Participants complete a fully online 16-week program via BruHealth that includes diabetes self-management education, personalized diet and exercise plans, self-monitoring tools (eg, glucometer and smartwatch), and support from health coaches through video consultations and instant messaging. Anthropometric and biochemical measures are collected at baseline and after the intervention. Data sources include the Brunei Health Information Management System, BruHealth app logs, and the health care professional portal. Descriptive statistics will summarize participant characteristics and outcomes. Paired 2-tailed t tests or Wilcoxon signed-rank tests will compare the results before and after the intervention. Subgroup analyses will explore outcomes based on glycemic changes, BMI, medication type, and program engagement. Participant feedback will be qualitatively analyzed. Fasting risk in Muslim participants will be stratified using the International Diabetes Federation-Diabetes and Ramadan Alliance Risk score. RESULTS: Recruitment began on August 20, 2024, following project approval in July 2024, and continued until July 2025. By the end of data collection on November 12, 2025, a total of 459 participants had been enrolled, and 422 (91.9%) had completed the program. Data analysis is currently ongoing, with results expected in early 2026. CONCLUSIONS: Self-management mobile health apps are promising tools for chronic disease management, including T2DM. The BALANCE program is Brunei's first national-scale study evaluating a fully online T2DM intervention. Localization to a region's population may support improved health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73964.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,047 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».